LIS Oyster Lab Tissue Processing

Updated 11/19/24 by K. Lenderman - all code up to date & graphs generated

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Warning: package ‘ggplot2’ was built under R version 4.3.3Warning: package ‘readxl’ was built under R version 4.3.3
Attaching package: ‘dplyr’

The following objects are masked from ‘package:plyr’:

    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Loading required package: lattice

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union


Attaching package: ‘purrr’

The following object is masked from ‘package:plyr’:

    compact


Attaching package: ‘tidyr’

The following object is masked from ‘package:reshape2’:

    smiths

This code chunk merges all .csv files within the Tissue processing folder into one data frame and outputs the full dataset into a .csv master file. This allows us to download the raw data as a .csv, add it to the repository folder, and create the master data file without copying and pasting data in excel.

reading in .csv files from local folder

#data_all <- list.files(path = "Lab_Data_TissueProcessing/raw_data/Files_by_Month",                           # Identify all CSV files
# pattern = "*.csv", full.names = TRUE) %>% 
#lapply(read_csv) %>%   # Store all files in list
#  bind_rows          # Combine data sets into one data set 
#data_all                                            # Print data to RStudio console


#as.data.frame(data_all)  # Convert tibble to data.frame


#Filtering NAs and unnecessary columns
#data_all <- data_all %>% filter(!is.na(date_collected))
#data_all <- select(data_all, -light_regime, -oyster_zone)


#write.csv(data_all, "Master_files/tissue_processing_all_data.csv", row.names=FALSE)




##### USE THIS CODE TO MERGE DATA FILES - some of the files have columns that are not the same format (dates specifically), which was causing issues in merging. This code below should solve that problem. It converts all date columns to dates and m/d/y format #####



file_paths <- list.files(path = "Lab_Data_TissueProcessing/raw_data/Files_by_Month",
                          pattern = "*.csv", full.names = TRUE)


data_all <- lapply(file_paths, function(file_path) {
  read_csv(file_path) %>%
    mutate(
      date_collected = as.Date(date_collected,format = "%m/%d/%Y"),
      date_processed = as.Date(date_processed,format = "%m/%d/%Y"),
      date_davidsons = as.Date(date_davidsons,format = "%m/%d/%Y"),
      date_etoh = as.Date(date_etoh,format = "%m/%d/%Y")
    )
}) %>%
  bind_rows

New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, oyster_zone, condition
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): light_regime, dissection_notes, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histolog...
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (18): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ...23, ...24, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 27── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl  (2): lab_sample, height_mm
lgl (18): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17, ...18, ....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 27── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl  (2): lab_sample, height_mm
lgl (18): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17, ...18, ....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (18): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ...23, ...24, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 27── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl  (2): lab_sample, height_mm
lgl (18): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17, ...18, ....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 998 Columns: 27── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl  (2): lab_sample, height_mm
lgl (18): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17, ...18, ....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 46 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (7): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, date_etoh, ...21, ...22, ...2...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 27── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl  (2): lab_sample, height_mm
lgl (17): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17, ...18, ...19, ...20, ....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (7): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 998 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, date_etoh, ...21, ...22, ...2...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (21): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, da...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (21): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, da...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (7): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, date_etoh, ...21, ...22, ...2...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (21): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, da...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (21): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, da...
date  (3): date_collected, date_processed, date_davidsons
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (7): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (18): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ...23, ...24, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (8): lab_id, date_collected, site, date_processed, condition, sample_notes, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (7): mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (18): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ...23, ...24, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (4): lab_id, site, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (20): oyster_zone, light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, .....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): lab_id, site, oyster_zone, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (19): light_regime, mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 999 Columns: 33── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): lab_id, site, oyster_zone, light_regime, condition, dissection_notes
dbl   (5): lab_sample, ww_total_g, height_mm, length_mm, width_mm
lgl  (18): mantle_rftm, ggr1_etoh, ggr2_etoh, adductor_etoh, cross_histology, ...21, ...22, ...23, ...24, ....
date  (4): date_collected, date_processed, date_davidsons, date_etoh
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.New names:Rows: 36 Columns: 17── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (7): lab_id, date_collected, site, date_processed, condition, date_davidsons, date_etoh
dbl (2): lab_sample, height_mm
lgl (8): sample_notes, mantle_rftm, ggr1_frozen, ggr2_etoh, cross_histology, ...15, ...16, ...17
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

as.data.frame(data_all)  # Convert tibble to data.frame

data_all <- data_all %>% filter(!is.na(date_collected))
data_all <- select(data_all, -light_regime, -oyster_zone)


View(data_all)

write.csv(data_all, "Master_files/tissue_processing_all_data.csv", row.names=FALSE)

#A wrong value caliper input was identified for the height of sample 0923GOLD_23. This code is removing that value from the data set as we cannot conclude what this original value was. The value is 8.62. This will cause this individual to fall out of the dataset when standardized to length. This code does not completely remove the individual from the dataset.

data_all$height_mm[data_all$height_mm == "0"] <- NA

adding a month & year column to the data

data_all <- data_all %>% dplyr::mutate(date_collected= as.Date(date_collected), month = month(date_collected))

data_all <- data_all %>% dplyr::mutate(date_collected= as.Date(date_collected), year = year(date_collected))

#changing numeric month to month name
data_all$month <- factor(data_all$month, levels = c("1","2","3","4","5","6","7", "8", "9", "10", "11", "12"),
        labels=c("Jan","Feb", "March","April","May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec"))

data_all

This chunk of code creates a numerical value in a new column for the body condition scores

data_all<- data_all %>%
  mutate(condition_score = recode(condition, 
                                     "1_very_good" = 1, "2_good" = 2, "3_good_minus"= 3, "4_fair_plus"= 4, "5_fair"= 5,"6_fair_minus"= 6, "7_poor_plus"=7,
                                     "8_poor"= 8, "9_very_poor"= 9))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `condition_score = recode(...)`.
Caused by warning:
! Unreplaced values treated as NA as `.x` is not compatible.
Please specify replacements exhaustively or supply `.default`.

head(data_all)

NA
NA

#This chunk of code is removing 0723LAUR_20 and 0723LAUR_26 from the datasheet as they have been identified as spat on shell to avoid bias in the data. During this sample collection there were animals that were significantly smaller than the single set oysters. These individuals should be removed from all monthly sampling related datasheets including disease analysis. All tissue amples will be disgarded.

data_all <- data_all %>%
  subset(lab_id != "0723LAUR_20") %>%
  subset(lab_id != "0723LAUR_26")

data_all

NA

Summary of all data - height

st_height <- summarySE(data_all%>% filter(!is.na(height_mm)), measurevar="height_mm", groupvars=c("site", "date_collected"))

st_height 

#Calculate completeness for QC
st_height$Completeness <- st_height$N /30

st_height

write.csv(st_height, "Lab_Data_TissueProcessing/output\\Completeness_tissue_processing_data.csv", row.names=FALSE)

#Mean Height

ggplot(data=data_all, aes(x=site, y=height_mm, fill=site)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Mean Shell Height ", x ="site", y = "Mean Shell Height (mm)") + facet_wrap(.~year)

#Body condition

ggplot(data=data_all, aes(x=site, y=condition_score, fill=site)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Mean Condition Score ", x ="site", y = "Mean Body Condition Score")+ scale_y_reverse()+ facet_wrap(.~year)

mean_body_condition <- data_all %>%
  dplyr::group_by(site, month, year)%>%
  dplyr::summarize(mean_bsc = mean(condition_score, na.rm = TRUE))

`summarise()` has grouped output by 'site', 'month'. You can override using the `.groups` argument.

mean_body_condition

#for overlay graph
mean_bcs_ashc <- mean_body_condition %>%
  filter(site == "ASHC") %>%
  filter(year == "2024")

#Mean body condtion scores - 2023
data_2023 <- data_all%>%  filter(!year=="2024")
  
ggplot(data=data_2023, aes(x=month, y=condition_score, group = month, fill = site)) +
  geom_boxplot()+ 
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = -35, vjust = 0.5, hjust=1))+
  labs(title="Mean Condition Score - 2023", x ="month", y = "Mean Body Condition Score")+ scale_y_reverse()+facet_wrap(~site)

#Mean body condtion scores - 2024
data_2024 <- data_all%>%  filter(!year=="2023")
  
ggplot(data=data_2024, aes(x=month, y=condition_score, group = month, fill = site)) +
  geom_boxplot()+ 
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = -35, vjust = 0.5, hjust=1))+
  labs(title="Mean Condition Score - 2024", x ="month", y = "Mean Body Condition Score")+ scale_y_reverse()+facet_wrap(~site)

#Proportions graph Body condition scores

#Proportions graph Body condition scores - 2023

df_BCS_proportions2023<- data_2023 %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%

  group_by(site, month, year, condition_score_bin) %>% dplyr ::summarise(Count= n()) %>%

  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

`summarise()` has grouped output by 'site', 'month', 'year'. You can override using the `.groups` argument.

df_BCS_proportions2023<- na.omit(df_BCS_proportions2023)

BCS_proportion_all_2023<- ggplot(data=df_BCS_proportions2023, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill", colour = "black")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title="Proportion of body condition scores - 2023", x ="month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
  scale_fill_brewer(palette = "Blues", direction = -1)+
  facet_wrap(~ site)

BCS_proportion_all_2023

#pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/all_BCS_proportion_2023.pdf"), height = 7, width = 13)
#print(BCS_proportion_all_2023)
#dev.off()

#Proportions graph Body condition scores - 2024
df_BCS_proportions2024<- data_2024 %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%

  group_by(site, month, year, condition_score_bin) %>% dplyr ::summarise(Count= n()) %>%

  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

`summarise()` has grouped output by 'site', 'month', 'year'. You can override using the `.groups` argument.

df_BCS_proportions2024<- na.omit(df_BCS_proportions2024)

BCS_proportion_all_2024<- ggplot(data=df_BCS_proportions2024, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill", colour = "black")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title="Proportion of body condition scores - 2024", x ="month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
  scale_fill_brewer(palette = "Blues", direction = -1)+
  facet_wrap(~ site)

BCS_proportion_all_2024

pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/all_BCS_proportion_2024.pdf"), height = 7, width = 13)

print(BCS_proportion_all_2024)
dev.off()

png 
  2 

# Proportion per month of intensity scores at three sites during 2023 & 2024
df_bcs_proportions_new <- rbind(df_BCS_proportions2023, df_BCS_proportions2024)

df_bcs_proportions_new<- df_bcs_proportions_new %>% filter(!site=="LAUR")

ggplot(data=df_bcs_proportions_new, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill",colour = "black")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(fill ="Body condition scores")+
  labs(title="Proportion of body condition scores", x ="month", y = "Proportion")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
scale_fill_brewer(palette = "Blues")+
   #scale_x_continuous("Month", breaks = c(3,4,5,6,7,8,9,10))+ 
  facet_grid(year ~site)

#Shell Pathology - all data #Includes 2023 data and all data that is in the 2024 format

shell_path <- list.files(path = "Lab_Data_TissueProcessing/raw_data/shell_pathology",                           # Identify all CSV files
 pattern = "*.csv", full.names = TRUE) %>% 
lapply(read_csv) %>%   # Store all files in list
  bind_rows          # Combine data sets into one data set 

shell_path <- shell_path %>% filter(!is.na(lab_id))

write.csv(shell_path, "Master_files/shell_pathology_all_data.csv", row.names=FALSE)

Ash Creek Summary

df_ASHC<- data_all%>%
  filter(site=="ASHC")
df_ASHC

## Shell Height ##
st_height_ASHC <- summarySE(df_ASHC, measurevar="height_mm", groupvars=c("date_collected"))
st_height_ASHC

## Body Condition ##
#Excludes April and May due to scoring change. These months are scored categorically 'fat, medium, watery'. 

st_condition_ASHC<- summarySE(df_ASHC, measurevar = "condition_score", groupvars = c("date_collected"))
st_condition_ASHC

mean_condition_ASHC <- st_condition_ASHC %>%
  filter(!is.na(condition_score)) %>%  # Filter out rows where condition_score is NA
  summarize(mean_bcs = mean(condition_score, na.rm = TRUE))
mean_condition_ASHC

NA

ggplot(data=df_ASHC, aes(x=month, y=height_mm, group= month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=" Ash Creek Mean Shell Height ", x ="month", y = "Mean Shell Height (mm)")+facet_wrap(.~year)

#Mean Body condition per month
ggplot(data=df_ASHC, aes(x= month, y= condition_score, group = month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Ash Creek Mean Body Condition Score ", x ="month", y= " condition score (1-9)") + scale_y_reverse()+facet_wrap(.~year)

#ASHC Proportions graph Body condition scores - all years

ASHC_BCS_proportions<- df_ASHC %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%
  group_by(site, month, condition_score_bin, year) %>% dplyr::summarise(Count= n()) %>%
  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

`summarise()` has grouped output by 'site', 'month', 'condition_score_bin'. You can override using the `.groups` argument.

ASHC_BCS_proportions<- na.omit(ASHC_BCS_proportions)

ASHC_BCS_proportions

BCS_proportion_ASHC<- ggplot(data=ASHC_BCS_proportions, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title="Ash Creek Proportion of body condition scores June- November", x ="month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
  facet_wrap(~ year)
 #scale_fill_brewer() +
  #facet_wrap(~ site)

BCS_proportion_ASHC

  
pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/ASHC_BCS_proportion.pdf"), height = 7, width = 13)

Error in pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/ASHC_BCS_proportion.pdf"),  : 
  cannot open file 'Lab_Data_TissueProcessing/output/ASHC_BCS_proportion.pdf'

% of scores >3 at ash creek - all years

df_ASHC

ASHC_precent_greater_3 <- df_ASHC %>%
  dplyr::group_by(month, site, year) %>%
  dplyr::summarise(Percentage = mean(condition_score <= 3)*100)

`summarise()` has grouped output by 'month', 'site'. You can override using the `.groups` argument.

ASHC_precent_greater_3

ASHC_BCS_percentage <- ggplot(ASHC_precent_greater_3,aes(x = month, y = Percentage)) +
  #geom_bar(width = 0.5, stat = "identity", position = "fill") +
    geom_col()+
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    axis.title.y = element_text(size = rel(1.3), angle = 90),
    axis.title.x = element_text(size = rel(1.3), angle = 0),
    axis.text = element_text(size = 12)
  ) +
  labs(
    title = "Ash Creek % of body condition scores >= 3",
    x = "month",
    y = "Percentage of body condition scores >= 3"
  ) +
    facet_wrap(~year)
  #scale_x_continuous( breaks = seq(5,12, by =1) )+
  #ylim(0,60)+ 
  #scale_y_continuous(limits = c(0,100), breaks = seq(0,100, by = 10))
ASHC_BCS_percentage

pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/ASHC_BCS_percentage.pdf"),height = 7, width = 13)

Error in pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/ASHC_BCS_percentage.pdf"),  : 
  cannot open file 'Lab_Data_TissueProcessing/output/ASHC_BCS_percentage.pdf'

Fence Creek Summary

df_FENC<- data_all%>%
  filter(site=="FENC")
df_FENC

## Shell Height ##
st_height_FENC <- summarySE(df_FENC, measurevar="height_mm", groupvars=c("date_collected"))
st_height_FENC 

## Body Condition ##
#Excludes April and May due to scoring change. These months are scored categorically 'fat, medium, watery'. 

st_condition_FENC<- summarySE(df_FENC%>% filter(!is.na(condition_score)), measurevar = "condition_score", groupvars = c("date_collected"))
st_condition_FENC

NA

ggplot(data=df_FENC, aes(x=month, y=height_mm, group=month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=" Fence Creek Mean Shell Height ", x ="month", y = "Mean Shell Height (mm)")+ facet_wrap(.~year)

#Condition distribution across all sample months - 2023
df_FENC.2023 <- df_FENC%>%  filter(!year=="2024")

ggplot(data=df_FENC.2023, aes(x= condition)) +
  geom_bar()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Fence Creek Body Condition index - 2023", x ="Condition categorization")+
    facet_wrap(~ month, scales = "free")

#Condition distribution across all sample months - 2024
df_FENC.2024 <- df_FENC%>%  filter(!year=="2023")

ggplot(data=df_FENC.2024, aes(x= condition)) +
  geom_bar()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Fence Creek Body Condition index - 2024", x ="Condition categorization")+
    facet_wrap(~ month, scales = "free")

#Mean Body condition per month
ggplot(data=df_FENC, aes(x= month, y= condition_score, group = month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Fence Creek Mean Body Condition Score", x ="month", y= " condition score (1-9)")+ scale_y_reverse() + facet_wrap(~ year, scales = "free")

#FENC Proportions graph Body condition scores - all years

FENC_BCS_proportions<- df_FENC %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%
  group_by(site, month, condition_score_bin, year) %>% dplyr::summarise(Count= n()) %>%
  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

`summarise()` has grouped output by 'site', 'month', 'condition_score_bin'. You can override using the `.groups` argument.

FENC_BCS_proportions<- na.omit(FENC_BCS_proportions)

FENC_BCS_proportions

BCS_proportion_FENC<- ggplot(data=FENC_BCS_proportions, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title="Fence Creek Proportion of body condition scores", x ="month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
  facet_wrap(~ year)
 #scale_fill_brewer() +
  #facet_wrap(~ site)

BCS_proportion_FENC

  
pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/FENC_BCS_proportion.pdf"), height = 7, width = 13)

Error in pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/FENC_BCS_proportion.pdf"),  : 
  cannot open file 'Lab_Data_TissueProcessing/output/FENC_BCS_proportion.pdf'

% of scores >3 at Fence Creek - all years

#removing 0823FENC_28 bcs was NA
df_FENC <- df_FENC %>% drop_na(condition)

FENC_precent_greater_3 <- df_FENC %>%
  dplyr::group_by(month, year) %>%
  dplyr::summarise(Percentage = mean(condition_score <= 3)*100)

`summarise()` has grouped output by 'month'. You can override using the `.groups` argument.

FENC_precent_greater_3

FENC_BCS_percentage <- ggplot(FENC_precent_greater_3,aes(x = month, y = Percentage)) +
  #geom_bar(width = 0.5, stat = "identity", position = "fill") +
    geom_col()+
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    axis.title.y = element_text(size = rel(1.3), angle = 90),
    axis.title.x = element_text(size = rel(1.3), angle = 0),
    axis.text = element_text(size = 12)
  ) +
  labs(
    title = "Fence Creek % of body condition scores >= 3",
    x = "month",
    y = "Percentage of body condition scores >= 3"
  ) +
    facet_wrap(~year)
  #scale_x_continuous( breaks = seq(5,12, by =1) )+
  #ylim(0,60)+ 
  #scale_y_continuous(limits = c(0,100), breaks = seq(0,100, by = 10))
FENC_BCS_percentage

pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/FENC_BCS_percentage.pdf"),height = 7, width = 13)

Error in pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/FENC_BCS_percentage.pdf"),  : 
  cannot open file 'Lab_Data_TissueProcessing/output/FENC_BCS_percentage.pdf'

Gold Star Beach Summary

0524GOLD has 46 samples - we attempted to sample from 2022 and 2023 planting and consider them seperate but it appeared that due to a storm the plantings mixed together.

```r
df_GOLD<- data_all%>%
  filter(site==\GOLD\)
df_GOLD <- df_GOLD %>% filter(!is.na(condition))

#2023 data
df_GOLD2023 <- df_GOLD%>%  filter(!year==\2024\)

#2024 data
df_GOLD2024 <- df_GOLD%>%  filter(!year==\2023\)


## Shell Height ##
st_height_GOLD <- summarySE(df_GOLD, measurevar=\height_mm\, groupvars=c(\date_collected\))
st_height_GOLD 

## Body Condition ##
st_condition_GOLD<- summarySE(df_GOLD, measurevar = \condition_score\, groupvars = c(\date_collected\))
st_condition_GOLD

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```r
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NA

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<!-- rnb-text-begin -->

#GOLD Proportions graph Body condition scores


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```r
```r

GOLD_BCS_proportions<- df_GOLD %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ \1\, condition_score == 2 ~\2\,condition_score == 3 ~ \3\, condition_score == 4 ~\4\,condition_score == 5 ~ \5\, condition_score == 6 ~\6\,condition_score == 7 ~ \7\, condition_score == 8 ~\8\, condition_score ==9 ~\9\, TRUE ~ as.character(condition_score))) %>%
  group_by(site, month, year, condition_score_bin) %>% dplyr::summarise(Count= n()) %>%
  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

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summarise() has grouped output by ‘site’, ‘month’, ‘year’. You can override using the .groups argument.

<!-- rnb-output-end -->

<!-- rnb-source-begin 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 -->

```r
```r
GOLD_BCS_proportions<- na.omit(GOLD_BCS_proportions)

BCS_proportion_GOLD <- ggplot(data=GOLD_BCS_proportions, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat=\identity\, position = \fill\)+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title=\Gold Star Beach Proportion of body condition scores\, x =\month\, y = \Proportion of body condition scores \)+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))+
 #scale_fill_brewer() +
  facet_wrap(~ year)
BCS_proportion_GOLD
  
pdf(paste0(path = \Lab_Data_TissueProcessing/output\ ,\/GOLD_BCS_proportion.pdf\), height = 7, width = 13)

print(BCS_proportion_GOLD)
dev.off()

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoicG5nIFxuICAyIFxuIn0= -->

png 2

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<!-- rnb-plot-begin -->

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" />

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# % of scores >3 at gold june - november 

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```r
```r
df_GOLD

GOLD_precent_greater_3 <- df_GOLD %>%
  dplyr::group_by(month, site, year) %>%
  dplyr::summarise(Percentage = mean(condition_score <= 3)*100)

<!-- rnb-source-end -->

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summarise() has grouped output by ‘month’, ‘site’. You can override using the .groups argument.

<!-- rnb-output-end -->

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```r
```r
GOLD_precent_greater_3

<!-- rnb-source-end -->

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"} -->

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```r
```r

GOLD_BCS_percentage <- GOLD_precent_greater_3 %>%
  ggplot(aes(x = month, y = Percentage)) +
  #geom_bar(width = 0.5, stat = \identity\, position = \fill\) +
    geom_col()+
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    axis.title.y = element_text(size = rel(1.3), angle = 90),
    axis.title.x = element_text(size = rel(1.3), angle = 0),
    axis.text = element_text(size = 12)
  ) +
  labs(
    title = \Gold Star Beach % of body condition scores >= 3\,
    x = \month\,
    y = \Percentage of body condition scores >= 3\
  ) +facet_wrap(~year)
  #+ scale_x_continuous( breaks = seq(5,12, by =1) )+
  #ylim(0,60)+ 
  #scale_y_continuous(limits = c(0,100), breaks = seq(0,100, by = 10)) 

    
GOLD_BCS_percentage

pdf(paste0(path = \Lab_Data_TissueProcessing/output\, \/GOLD_BCS_percentage.pdf\),height = 7, width = 13)

print(GOLD_BCS_percentage)
dev.off() 

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoicG5nIFxuICAyIFxuIn0= -->

png 2

<!-- rnb-output-end -->

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<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["month"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["site"],"name":[2],"type":["chr"],"align":["left"]},{"label":["year"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["Percentage"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"April","2":"GOLD","3":"2024","4":"3.333333"},{"1":"May","2":"GOLD","3":"2023","4":"6.666667"},{"1":"May","2":"GOLD","3":"2024","4":"21.739130"},{"1":"June","2":"GOLD","3":"2023","4":"66.666667"},{"1":"June","2":"GOLD","3":"2024","4":"0.000000"},{"1":"July","2":"GOLD","3":"2023","4":"10.000000"},{"1":"July","2":"GOLD","3":"2024","4":"0.000000"},{"1":"Aug","2":"GOLD","3":"2023","4":"43.333333"},{"1":"Aug","2":"GOLD","3":"2024","4":"16.666667"},{"1":"Sept","2":"GOLD","3":"2023","4":"3.333333"},{"1":"Sept","2":"GOLD","3":"2024","4":"0.000000"},{"1":"Oct","2":"GOLD","3":"2023","4":"13.333333"},{"1":"Oct","2":"GOLD","3":"2024","4":"16.666667"}],"options":{"columns":{"min":{},"max":[10],"total":[4]},"rows":{"min":[10],"max":[10],"total":[13]},"pages":{}}}
  </script>
</div>

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uBpM0DijmseYKgAQcBnh0sNFcDTToGa5XmACsCgAngMCiwfVtMpVqxmUaECMKgAHsMYoap8XKw2WR8jZJMHGCpAELQfI8Q5RXsMFcBjPUDCIg8wVIAgMCgQTaKi3r9tnQcYKkAQpPcFpsIGkGgQ2cyrSwVgUAE86QrIWVTUAAmrWeWoAAwqgMc0QOKiGiCxjwMkegsVwMMBEqWGCuDhAIlSQwXwtFPgvctXnPIAQwUIAoMCt3ZfUxOp8HmB3kIF8JgmUZFvXfWOT/Cpsd5CBfCYnx2eEacAvn2+x1ABPOYBElPeDg6Q6DVUAI9RAdEOOkkFeg0VwGNoCFXOqeFBbAj1GCqAxzSJinrtdo3d4R5DBfAYJlG56Hnb5GNjvCjaW6gAnrZ3h2t3nfIAQwUIAg6QKDVUAA8VKDVUAA8VKDVUAA8VKDVUAA8VKDVUAA8VKDVUAA8VKDVUAI9JgdqdEJs7A1QABhXAY7g7PBm9d9hqCgkqAIMK4DHNI+RtG1Uc4zC5XkIF8BgGS1tNp9ucBxgqQBC0n1PULQ8wVIAgMDwvQAXKARXAY3pewOY5geY8wFABgiBdgeUJb+9lxRV2h3sJFcBj6g7zomgpoAJ40vsCZ0c1vCjaU6gAHg6QKDVUAA8VKDVUAI9JgeWzQ0NDx6465QGGChAEBgVmot6w1bVRKgCDCuAxTavr7b8bPHiZ7x3uMVQAj2la3fDwP8PX7fUWKoCn/RghzinaY6gAHipQaqgAHvP7BSQLVq+ZoQIwqAAe0zC5LfJ66PIIp9XtLVQAj+HByYno1ducVre3UAE8hvsCtUvq1dvH+ert3kIF8LQZIMFXb/ceKoCHY4RKDRXA06JA7faVudrtyxo+MtNTqACeFgWiV+3xkZlSQAXwtJ4Fzh6b4yMzZYEK4GFfoNRQATzpd4dv6y7A7Nd4FuglVAAPxwiVGiqAp1WBm6OjR6uVPaoncJTd4d5CBfC0KrBU9RLsc8gDDBUgCFIaQrcv/7Ba+Ya6K/CK1YuHqQAMKoDHMI+QzbXQljzAUAGCgBdFSw0VwMMBErkKKLhqRqgAHg6QyFVAwVUzQgXwcIBErgIKrpoRKoCHfYFcBRRcNSNUAA8VyFVAwVUzQgXwtCrw4E6SQXrvMBUYSFK6w8mbw+wOU4F+J6U7HA4O8vaMHq16e9gdpgJ9TnpfYKmqZk+pTVXOOeQBhgoQBIbZ5KJdP1LBMg8wVIAgcHpeYH38kPxn9bTvn7rRmgcYKkAQGBRIPwtM+1KB9XFf8MibLXmAoQIEQXpfYCqcSTF+z0DIvK8UmPefur/xmv90Sx5gqABBkK6AvDK6e/RI45yiK2NfkQ2hjfMH7+u/jXmAoQIEgWlO0cnwmbGEAaIjoPoCUYdgut4SogIwqAAe8wCJB013hqcP3ld7/8qYagLND19Q6SXgdYygAgSB9RihRbHLpyiQzAMMFSAITArUmsYIqR2fClCB/iNdgagrkBgjNO+HDF9YH1cKsC/QDagAHtNFUW9b4yMzdQV4RYgK9BWGW2Ppr9wOLwZNy/sC13lfoBtQATztB0g0ESrAu8NUoJ8wDJNrp4AaI3SAY4S6ARXAk94XmLF62WpzHmCoAEGQrsDyhLeX8whRgYHA1B3mg5NUYEAwzCnKeYSowKDASVRyFVBw1YxQATxUIFcBBVfNCBXAY1Jg+ezQ0NCxq055gKECBIFBgZmoN2x1bZQKwKACeNIVWPC8/XeDBy976QMlDHmAoQIEgeHucHT4n/F2OOQBhgoQBE6TqGTkAYYKEARUIFcBBVfNCBXAY2oIhX2ABY+zyVGBPsc0TG6LvB66PGJ1SYgKwKACeAwPTk543tahXZ7VSYAK4KACeEzzCF2S76CvHLd6/TAVgEEF8LQZIHHHNQ8wVIAg4BihXAUUXDUjVABPOwVqlucBKgCDCuAxKLB8eMs1Ocf6CZc8wFABgsAwRqgqHxeTE2pxjBAV6HPajxHirTEq0PdwgESuAgqumhEqgMegQPyiJSpABfoc05yi+kVLHCxNBfqcdAXkLCpqgITNSYAK4KACeEwDJC6qARL7OECCCvQ7HCCRq4CCq2aECuDhAIlcBRRcNSNUAA8VyFVAwVUzQgXwUIFcBRRcNSNUAA8VyFVAwVUzQgXwUIFcBRRcNSNUAA8VyFVAwVUzQgXwUIFcBRRcNSNUAI9hjNDu53PkAYYKEATpCkx4nrf3Fdc8wFABgsA4g8Quz6vs5eTqVKDvMfcFHsh5VCrH79rnAYYKEARtu8PL/ygaRLszTwVUAAYVwGNWoDZ72PO2/sUur5L1/DAVgEEF8Jj6AnL/r+yTraBbmc8PUwEYVABP+uPzqgEUXRfNfn6YCsCgAnhMzw7XZxNdO7KfZwEq0L+knwUsLgOl5AGGChAEHCCRq4CCq2aECuBpVeDBnSQ25wMqAIMK4GlRQE4ekYDzCFGBPqdFgdrZUcFRz9szerTq7TnGCRWpQH+T3hdYqoZTaU1Fs8rZ5QGGChAEhml14wkVOa0uFehzOK1urgIKrpoRKoAna1pdngWoQJ+TNa0u3ztMBfoc87S6u0eP8L3DVKD/MY0UnVR3BTitLhXoe9o8NWZ1ZziZBxgqQBBwjFCuAgqumhEqgMekwPLZoaGhY3x8ngr0PQYFZqIhQjYXhKgADiqAJ12BBc/bfzd48DLfO0wF+h7DAIno8D/D1+1RgX6HAyRyFVBw1YxQATxUIFcBBVfNCBXAY2oIhX2ABavbw1QABhXAk94dnvG2yOuhyyMcI0QF+p10BcRpIHz1NscIUYF+xziztHr19nGOEaIC/U6bARJ89TYVGAQ4RihXAQVXzQgVwGNS4L3JoaEhvmKDCvQ/BgUucowQFRgQTBdFKydEn/hmlWOEqEC/w1tjuQoouGpGqAAeDpDIVUDBVTNCBfAYzgJUoD8UKGGVSoepLxD2gxc4QIIK9DuGydVf9vaJv5eq+zm5OhXoc+wnV3//cd8/cEN+Wj3t+6dutOYBhgq4U8IqlQ7D5Oox8eTqi75k+EIQrI/LT4+82ZIHGCrgTgmrVDpsB0hsnB9+Kdh4zT8UBPP+U/fFp6db8gBDBdwpYZVKh60CK2Nyl984f/C+/H/4qTkPMFTAnRJWqXS4DZPbOH9ItIMOyY/T9ZYQFYBBBfC4KTDvPxedD4J52S0Q6SWYVWuGCrhTwiqVDicF7smuQKMCyTzAUAF3Slil0uGgwMZ1X7b/qQAV6CtaL4peMjwtuXE+vAi0Pq7+YV+gG1ABPCm3xrZcWzuyu2Vo0Pr48Bn1gVeEqEBfkaJA5Vza6LjpuOEzLe8LXOd9gW5ABfC0NoQmPG+XnEIlRJ8OVsbU3WF5U5h3h6lAP9HaHV46nDZGaN6PFVBjhA5wjFA3oAJ42j8y45YHGCrgTgmrVDrSH5k5e8xqDq2mPMBQAXdKWKXSwXmEchVQcNWMUAE8JgX4rrFS7C9UAI9BAb5rrBz7CxXAk64A3zVWkv2FCuAxzSPEd42VYn+hAng4j1CuAgqumpF+U6CM25AK5Cqg4KoZoQK4AnQSTqiYq4CCq2aECuAK0ElMU2nxXWPF558DKoArQCdJV4DvGkPknwMqgCtAJzHcF+C7xgD554AK4ArQSdoMkOC7xgrOPwdUAFeATsIxQrkKKLhqRqgArgCdhArkKqDgqhmhArgCdBIqkKuAgqtmhArgCtBJqECuAgqumhEqgCtAJ6ECuQoouGpGqACuAJ2ECuQqoOCqGaECuAJ0EiqQq4CCq2aECuAK0ElMCtR+MHpsrnaFt8YKzD8HVABXgE5iUOBWVc6fsjbCARK9yV8XQAVwBegkpqfGKl+X46SnOEyuN/lTgRIMllaPCixVOVi6J/lTgRI8MqMU4CMzPcqfClCBFMoYPnQFqACuAJ3E0BCKZpfmU2M9yp8K9Lo7PONt/4VQYLbK7nBv8qcCvVYguBhNpWUzhwoVwFWACuAK0ElMt8ZmjwgBhk5YFUsFYBWgArgCdBIOkMhVQLcqQAVwBegkVCBXAd2qABXAFaCTpCvw4I7mrkWxVABWASqAK0AnMdwXSLxqaVvmDOtUAFYBKoArQCdJvy9w9qjn7R4VPeLK7+7yKucyiqUCsApQAVwBOkl6QygaG6RmmM6eXZoKwCpABXAF6CTGu8Pqg1Qhe6hcWRVALw+vABVAFqCTZM8snT1OiAqAKkAFkAXoJAYF4rMAFWgbPmwFqACyAJ0kvS8wFQ6Pq02JfkD2UDkqAKoAFUAWoJOkK7A24lX2jB6tqocnM4fKUQFQBagAsgCdxHB3eHlS3RPYOyds2Js1XpoKgCpABZAF6CTmARJ3bttOLU0FQBWgAsgCdJJ+HiOEXh5eASqALEAnMSkQjhK6/cOvbuIHJ9HLwytABZAF6CSG7vBhPUJoMz87jF4eXgEqgCxAJzFdFPV2ed5Wz6vYPDRDBUAVoALIAnQS4zxC4SP0m/rZYfTy8ApQAWQBOol5gMSUd3KTzyCBXh5eASqALEAnMSswI84Am3seIfTy8ApQAWQBOomhIaTmENpBBbLCh60AFUAWoJOYusMnooHSVKBt+LAVoALIAnQS0yMzqjNQsZtIKOf2cQ2Hc/jQy6MrHFABZAE6iWmM0J9fk9eFvMzxQck8cGtLBfKFmApY5N92gETNrlgqgKlwQAWQBegk6d3h2/oNS7Nfw10UdQ2Hc/jQy6MrHFABZAE6SfaDk9nFUgFMhQMqgCxAJ2lV4Obo6NFqZc+o5ChyjJBrOJzDh14eXeGACiAL0ElaFViqJubR8vZZFEsFMBUOqACyAJ0kpSF0+/IPq5VvXJa8YjOfIhUAVTigAsgCdBLDbHLHrF443JQHbm2pQL4QUwGL/Hv41JhrOJzDh14eXeGACiAL0ElMCtTwM0u7hsM5fOjl0RUOqACyAJ0kXYHaZBeeGnMNh3P40MujKxxQAWQBOonxqbFt6qroqE2ngApgKhxQAWQBOonp/QInHYqlApgKB1QAWYBO0v7usB1UAFPhgAogC9BJzI/M2DOoCriukOvyQYsCzjm4l4glRwhgBegk6X2BGavH5pvzwK0tFcgXMipgkX+6AssT3l51e/jyFXaHi1ieCuSsALAAncTUHeZF0UKXpwI5KwAsQCcxDJAY1fCiaCHLU4GcFQAWoJNwgERXlqcCOSsALEAnoQJdWZ4K5KwAsACdxDhG6AeiDVSz6QxTAdsErssHVABZgE5iUOBW1VMvWbKZT5EKWCZwXT6gAsgCdJJ0BRa8ytfDeUWB0+q6hsM5fCVangrkrACwAJ3EOLO0GiSR/trt1dO+f+pG/TsVsEvgunxABZAF6CTGMUJKgdTBQuvjvuCRN+MfqIBdAtflAyqALEAnyaHAvP/U/Y3X/KfjH6iAXQLX5QMqgCxAJzE0hOTrNdTs0q0NoY3zB+/rv4154NaWCuQLGRWwyN80TG77L4QCs9WU7vD6+CH5z3S9JUQF7BK4Lh9QAWQBOonhoujFaIhQysTSK2OqCTQ/fEGll+RaWWJBp88LkGxMt8ZmjwgBhtJetteoQDIPUjhUAI/7AAkq0EWoAB53BdbHlQKd9wVINlQATzsFanfSUhR2RYhkQwXwGBRYPqzuCqS+eXta3he43vl9AZINFcBjGCNUlY+LyQm1UiZTKeruMMmGCuAxjREK7wekv3pbjhE60PkYIZINFcDTfh6hUr13eBChAngMClTOqQ+leu/wIEIF8JjmFFUNINEgAr53mGRDBfCkKyBnUdk6tMtuDhVuHxxUAI/homjtonzjWGUf8tlhkg0VwNPm1ljqjbF2eZDCoQJ4ejitLsmGCuApZHJ1AoMhxpOmgHxqLJ9RKX5s8uXLt0LdL7FsIcBUoPldYxWHmaU7L73Ey5dvhbpfYtlC0AUFHGeW7rz0Ei9fvhXqfollC0EXFHCcWZqQPqCTaXUJ6QOoABlwOplZmpA+oJOZpQnpAzqZWZqQPsBlZmlC+hCHaXWdiGYdIkAY40JAKbBx3nfePutfeDN7oU5wK2D9mQvZC9VZ+ev72Qt1sHwrZYyxY/5uMcaE2H5maUdybJ9F/zmHpVdffOLUS8gCPjg97LR9xg5+4LJBXZdPAR1jeIgdY4wJsf3M0g6oM7T79klO05XJ4tjwF8d8hwSOBSyO+b7v5MA93211XJdvpAsxRofYPcaQENvPLO3AxrflhnHfPg6HEDXV+8a0fwZUwMrYk/eDVadj1Pq42/ZxXb4RfIzRIc4RY0iI7WeWdmP9ezm2j8MhZF5O6TXvP7fxBqaAabXgxnmH7bP6pX+330Cr33nii591WD4NcIzRIc4RY0iIIQMkVp75vpp40X77rJ8+IBud89bRkOETm8cmhWoyuBYQbh6R1i7B6jNPPHFGnngtN5BoYzwucrZevhV8jNEhdowxLMQIBdbHh8+EW8Z6+2y8PuY/eka/ycYCEWi5eYLF7PCplXAtIJpAW6S12T6LYwfCVrMK+K8yl49Xw3L5tBzgMUaH2C3GuBAnFFietJ47on3hP/n8Bb1l7I9RG/ce94fPWB9CVkRPSjY6bRJEq2JdwPoXLoj8D4Vn6S+PPZJ5mW/jvKzkfLiBDjyb3Rqej/L8vxt2y7euYhdijAyxc4yBIa4rIJ+X2eV52zp9VkZ0oFQM9PbJ3Dwrp6OZSt8/7fu+9WnAPygSrVodcvROYlmAOjOraAf3hi8sZu5g6298/kK4SmLJe2P+k5klRE0AeaSyWr6Z7sQYGGLXGCNDXFdAzSQXT6zbASIah6J/bY5Oi2OPPqtbg6sv+tnH3Ghzzvv+qWxysOUAAAb1SURBVGcsGobyeBOvilUB4fa57vufeVwki14tYmbjenQij97A0PYkvfrGG7+qH1jloar98qYysTHGh9gtxtAQxwpEj84XcFMsjobV9lkZe1J1XS7or1lJROyeVQH54JmxR/8+u2ukYl1fFatRBSrNB+GNoZWxrJNunPl81rZXx0j/wEsrY+F+NZ+7NwyNcTdC7BZjZIhjBaIxER0OjQi77fH2+XZ2LNQJa/rRePtMZ6zyxnnRgLS9UKNo2kDtC5AXGoPkVQqbi3wic7UJs7bPvNg0H37wou8/vaiOsqtZJ5hU4DEGhzhXjIEhLlYB3W23OzZ9WvXoDqlrDot+4sTVhqhR2MEGaluAaGSHS4Xr8/6n/2bsQNv1CccQbJwfPpPdal70VfdPvqfhadHG+MyzY3lOAvAYg0PsHGN0iAtVoN5tt9g+6+MqAtMH3pQnTpFApbjXfp3jRqHdBkoeb6JVyipAr/i0/8gNecB9sm0I9RgCufqPZ7SaN87rPWN9fPiCPJZbtDFScgHHGB5ixxjDQ1ykAsluu8VVimk1PuSj++o0uHH+zH/IH1e/22adRbdL9wOzNtDKPwTNx5twldoWENS3z/RXvpdVgeQYApHsYEa/NtFEXhmzvfDVUiI4xt0IsVOM8SEuUIGmbnsmK2NfVjFWlwPUGTs7xfCF+BCitoBxXcKlGo83doRprNYnOYYg+5icHD0zbXPRJG3l0DHuSohdYowPcUKByp7R0dGjVfVPrnmE7Lvt0Uoe/B+5fdbHD96wu/jcuIHasn66YQNlHW9Wxg7Eo4JFmlPPZh5D/vuNnzaOIchcr+RlD/tRBE3AYwwLsXuMuxTihAJeklznAttue4RY4egOifUY2JwbKPN4Mz/8uD+sm6TiUOu37wLIuy1ynRvGEIiizrRdo/F6JXMrgI8xKsSuMe5aiOv3BW5fTuIyjUpdb7tue4wwPLw6/N3sBzMS3S7XDWRxTBeNSHlB+dRPs/OVzPsHfvrh9eeaxhBs/Gv7VNPxadppVH20gvgYY0PsGOPuhbiIYXIJvW267aJWn70RfXjO4rpDa7fL5lp4fQNlH9ODsOW4+qLvP3rGInAr8SUPlzEE8ipFtIGyB100g41xV0LsFOMuhrgIBZJ6W3XbxRku3ECyoZe1ffJ3u6INZEV4LWGxvmrt0D2tD77zpRctxxBEhfhPySWzrhqmrx8uxt0JsVOMuxjiQgZLJ/W2OIWKc9yLYyoKqgGZdT/dtdvVkNJ2EKa6oiwOPf/7+pifOWYrPCKtqlvxf2Q3hiBkRe7Fz7o+jKiAxrgrIXaJcTdDXIgCDXpbbR8VBrGBpmVNP8pY3LHblUw5bn3xcdF/Ljr5vt/uUmAQRM1MdfL/8AOXx8qCcDix7/pIugIb466E2CHG3QxxIQo06p3VbQ+im5tyA/3EKtpu3a6GgqzDJ3qZ1ufOef/RZ/1w9HH2ILqCAMe4GyF2iXEXQ1zMU2ONemd124Po2Qe5gezGrrt1u5Ll2G+feYdz53+N+dEFmq4pgI5xN0LsEuPuhbgYBVwOoSFhMMQGsutNOXW79Do9+eFr1k/gNF5Ttmc69wV+V9Ax7kKI88UYHeKCnh12OYTmSuHS7QqRxzOnMuZzPMB4z2UH6BB0jLsQ4jwxhoe4IAVy6O3qgEO3K2L1Dcs7XSGOY9f+7aXgo+udzYLiBjzG+BA7xrg7IS5qBokcejs6kH9MgS1OZ1x15d2x2dwh8BjjQ+wU4y6FuCgF8gz/nXdqe+K3T9uBkS189PoXv2Q9NLIQ4DHuggJOMe5OiAubRwjZaXHudvUpwBgPcIgLU8DtEOqGe7erPwHGeIBDvEneOOna7SLODGyIN4kChKCgAmTAoQJkwKECZMChAmTAoQJkwKECZMChAmTAoQJkwKECm4Plr4k/U+oVEKRYqMCmYKkq3wRNBRBQgU0BFcBBBTYFVAAHFegptYktP7roefuC2qT4q+ZxvbXL87aekJ+mKt8Uv257Pghm5EzHD0sFblbVD6Q4qEBPEQp8Uu7eJybCnVy+8lMhX3o4FX4Uh/5YgaPRD6Q4qEBPEXv8lqvBLbHLXw1uyv1+xquIg/xsVeowJT+LJXbEDaHoh47fi0sSUIGeIvbnk+r1JufCF/xEr74Vu7zQYUrt6wtJBeIfSGFQgZ4S7vKiOXQtVGBtJHzts/ol7P2qvT/ZHQ4/k6KgAj0l3PnTFBB7OxXoClSgp5gV4FmgW1CBntKsQGNfgAp0AyrQU5oVaLwilFTg1+aoAAYq0FNaFGi4L5BUILo1FlCBoqECPaVFgca7w/U9/mbV20EFIFABMuBQATLgUAEy4FABMuBQATLgUAEy4FABMuBQATLgUAEy4FABMuBQATLg/D/qOveMzuNTVQAAAABJRU5ErkJggg==" />

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2dwbG90KGRhdGE9ZGZfR09MRCwgYWVzKHg9bW9udGgsIHk9aGVpZ2h0X21tLCBncm91cD0gbW9udGgpKSArXG4gIGdlb21fYm94cGxvdCgpKyAgI3NjYWxlX2ZpbGxfbWFudWFsKHZhbHVlcz1jKFxcZm9yZXN0Z3JlZW5cXCxcXG9yYW5nZVxcLCBcXHB1cnBsZVxcKSkrXG4gIHRoZW1lX2J3KCkgKyAgdGhlbWUocGFuZWwuZ3JpZC5tYWpvciA9IGVsZW1lbnRfYmxhbmsoKSwgcGFuZWwuZ3JpZC5taW5vciA9IGVsZW1lbnRfYmxhbmsoKSkrIFxuICAjdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGUgPSA5MCwgdmp1c3QgPSAwLjUsIGhqdXN0PTEpKStcbiAgbGFicyh0aXRsZT1cXEdvbGRzdGFyIEJlYWNoIE1lYW4gU2hlbGwgSGVpZ2h0IFxcLCB4ID1cXG1vbnRoXFwsIHkgPSBcXE1lYW4gU2hlbGwgSGVpZ2h0IChtbSlcXCkgKyAjc2NhbGVfeF9jb250aW51b3VzKGxpbWl0cz0gYyg0LDExKSwgYnJlYWtzID0gc2VxKDUsMTAsIGJ5ID0xKSkrXG4gIGdlb21fc21vb3RoKG1ldGhvZCA9IFxcbG1cXCwgc2UgPSBGQUxTRSlcblxuYGBgXG5gYGAifQ== -->

```r
```r
ggplot(data=df_GOLD, aes(x=month, y=height_mm, group= month)) +
  geom_boxplot()+  #scale_fill_manual(values=c(\forestgreen\,\orange\, \purple\))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=\Goldstar Beach Mean Shell Height \, x =\month\, y = \Mean Shell Height (mm)\) + #scale_x_continuous(limits= c(4,11), breaks = seq(5,10, by =1))+
  geom_smooth(method = \lm\, se = FALSE)

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<img 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```r
```r
#Condition distribution across all sample months - 2023
ggplot(data=df_GOLD2023, aes(x= condition)) +
  geom_bar()+  #scale_fill_manual(values=c(\forestgreen\,\orange\, \purple\))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=\Goldstar Beach  Body Condition Score - 2023 \, x =\Condition categorization\)+
    facet_wrap(~ month, scales = \free\)

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<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r

#Condition distribution across all sample months - 2024
ggplot(data=df_GOLD2024, aes(x= condition)) +
  geom_bar()+  #scale_fill_manual(values=c(\forestgreen\,\orange\, \purple\))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=\Goldstar Beach  Body Condition Score - 2024\, x =\Condition categorization\)+
    facet_wrap(~ month, scales = \free\)

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

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```r
```r

#Mean Body condition per month
ggplot(data=df_GOLD, aes(x= month, y= condition_score, group = month)) +
  geom_boxplot()+  #scale_fill_manual(values=c(\forestgreen\,\orange\, \purple\))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  labs(title=\Goldstar Beach  Mean Body Condition Score \, x =\month\, y= \ condition score (1-9)\)+ scale_y_reverse() + facet_wrap(~year)

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuICAjdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGUgPSA5MCwgdmp1c3QgPSAwLjUsIGhqdXN0PTEpKStcblxuYGBgXG5gYGAifQ== -->

```r
```r
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+

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# Laurel Hollow Summary - NO LONGER A STUDY SITE

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```r
```r
```r
df_LAUR<- data_all%>%
  filter(site==\LAUR\)

## Shell Height ##
st_height_LAUR <- summarySE(df_LAUR, measurevar=\height_mm\, groupvars=c(\date_collected\))
st_height_LAUR 

## Body Condition ##
## Excludes May due to scoring system change. May scored fat, medium, watery. 
st_condition_LAUR<- summarySE(df_LAUR, measurevar = \condition_score\, groupvars = c(\date_collected\))
st_condition_LAUR

ggplot(data=df_LAUR, aes(x=month, y=height_mm, group= month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title=" Laurel Hollow Mean Shell Height - 2023", x ="month", y = "Mean Shell Height (mm)")

#Condition distribution across all sample months
ggplot(data=df_LAUR, aes(x= condition)) +
  geom_bar()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(title="Laurel Hollow Body Condition index - 2023", x ="Condition categorization")+
    facet_wrap(~ month, scales = "free")


#Mean Body condition per month
ggplot(data=df_LAUR, aes(x= month, y= condition_score, group = month)) +
  geom_boxplot()+  #scale_fill_manual(values=c("forestgreen","orange", "purple"))+
  theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  labs(title="Laurel Hollow Mean Body Condition Score - 2023", x ="month", y= " condition score (1-9)")+ scale_y_reverse()
  #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+

#LAUR Proportions graph Body condition scores - 2023 (only sampled one year)

LAUR_BCS_proportions<- df_LAUR %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%
  group_by(site, month, condition_score_bin, year) %>% dplyr::summarise(Count= n()) %>%
  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

LAUR_BCS_proportions<- na.omit(LAUR_BCS_proportions)

LAUR_BCS_proportions

BCS_proportion_LAUR<- ggplot(data=LAUR_BCS_proportions, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill")+  
   theme_bw() +  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  labs(title="Laurel Hollow Proportion of body condition scores - 2023", x ="month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.3), angle =90), axis.title.x = element_text(size = rel(1.3), angle = 0))+
  theme(axis.text=element_text(size=12))
  #facet_wrap(~ year)
 #scale_fill_brewer() +
  #facet_wrap(~ site)

BCS_proportion_LAUR
  
pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/LAUR_BCS_proportion.pdf"), height = 7, width = 13)
print(BCS_proportion_LAUR)
dev.off()

% of scores >3 at LAUR june - november

#removing 0823FENC_28 bcs was NA
df_LAUR. <- df_LAUR%>%filter(!row_number() %in% c(2))

LAUR_precent_greater_3 <- df_LAUR. %>%
  dplyr::group_by(month, site, year) %>%
  dplyr::summarise(Percentage = mean(condition_score <= 3)*100)
LAUR_precent_greater_3

LAUR_BCS_percentage <- LAUR_precent_greater_3 %>%
  ggplot(aes(x = month, y = Percentage)) +
  #geom_bar(width = 0.5, stat = "identity", position = "fill") +
    geom_col()+
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    axis.title.y = element_text(size = rel(1.3), angle = 90),
    axis.title.x = element_text(size = rel(1.3), angle = 0),
    axis.text = element_text(size = 12)
  ) +
  labs(
    title = "Gold Star Beach % of body condition scores >= 3",
    x = "month",
    y = "Percentage of body condition scores >= 3"
  ) #+facet_wrap(~year)
  #+ scale_x_continuous( breaks = seq(5,12, by =1) )+
  #ylim(0,60)+ 
  #scale_y_continuous(limits = c(0,100), breaks = seq(0,100, by = 10)) 

    
LAUR_BCS_percentage

pdf(paste0(path = "Lab_Data_TissueProcessing/output", "/LAUR_BCS_percentage.pdf"),height = 7, width = 13)
print(LAUR_BCS_percentage)
dev.off() 

#Coding graphs for LIS Conference - only 2023 data
data_2023 <- data_all%>%  filter(!year=="2024")

df_BCS_proportions<- data_2023 %>%
  mutate(condition_score_numeric = as.numeric(condition_score),condition_score_bin = case_when(condition_score == 1 ~ "1", condition_score == 2 ~"2",condition_score == 3 ~ "3", condition_score == 4 ~"4",condition_score == 5 ~ "5", condition_score == 6 ~"6",condition_score == 7 ~ "7", condition_score == 8 ~"8", condition_score ==9 ~"9", TRUE ~ as.character(condition_score))) %>%
  group_by(site, month, condition_score_bin) %>% dplyr ::summarise(Count= n()) %>%
  ungroup() %>%
  mutate(Proportion = Count/sum(Count))

df_BCS_proportions<- na.omit(df_BCS_proportions)

df_BCS_proportions$month <- factor(df_BCS_proportions$month, levels = c("5","6","7", "8", "9", "10", "11"),
        labels=c("May", "June", "July", "Aug", "Sept", "Oct", "Nov"))
df_BCS_proportions$site <- factor(df_BCS_proportions$site, levels = c("ASHC","FENC","GOLD", "LAUR"),
        labels=c("Ash Creek", "Fence Creek", "Gold Star", "Laurel Hollow"))

bcs.prop.2023 <- ggplot(data=df_BCS_proportions, aes(x=month, y= Proportion, fill=condition_score_bin)) +
  geom_bar(width = .5, stat="identity", position = "fill", colour = "black")+  
   theme_bw() +  theme(panel.grid.minor = element_blank())+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = rel(.9)))+
  theme(panel.grid.major.x =element_blank())+
  labs(title="Body condition scores in 2023", x ="Month", y = "Proportion of body condition scores ")+ theme(axis.title.y = element_text(size = rel(1.2), angle =90), axis.title.x = element_text(size = rel(1.2), angle = 0))+ scale_fill_brewer(palette = "Blues") +
  theme(strip.text = element_text(size = 15))+
  theme (legend.title = element_text(size = 18))+
  theme(title = element_text(size = 17))+
  theme(axis.text=element_text(size=17))+
  labs(fill ="Body Condition Score")+
  facet_wrap(~ site )
bcs.prop.2023

#pdf(paste0(path = "Lab_Data_TissueProcessing/output" ,"/BCS_proportion_2023.pdf"), height = 7, width = 13)
#print(bcs.prop.2023)
#dev.off()

#BCS_proportion_all+scale_fill_manual(values = c("#003C30","#01665E","#80CDC1","#C7EAE5","#F6E8C3","#DFC27D","#BF812D", "#8C510A","#543005" ))
#scale_fill_manual(values = c("#003C30","#01665E","#80CDC1","#C7EAE5","azure1","slategray1","slategray3", "slategray4", "gray25" )) 

f <- function(pal) brewer.pal(brewer.pal.info[pal, "maxcolors"], pal)
(cols <- f("YlGnBu"))

#Coding graphs for LIS Conference - only 2023 data (continued)

df_3_prop<- select(data_2023, -c("site"))

#Count and proportion table for 2023 bcs scores by month
bcs_table <- table(data_2023$condition_score, data_2023$month, data_2023$site)
names(dimnames(bcs_table)) <- c("Body Condition Score", "Month", "Site")
bcs_table <- addmargins(bcs_table)
bcs_table

sessionInfo()

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